Integrating Devil Scrapes Actors — n8n, Make, Zapier, Sheets

You don’t need to write a client at all to put a Devil Scrapes Actor into a workflow. Apify has first-party or community integrations for the automation tools most teams already use, plus a plain URL-based export path for anything without a dedicated connector. This page covers the practical wiring for each.

n8n

Apify publishes an official Apify node for n8n. Add it to a workflow and configure:

• Resource: Actor
• Operation: Run Actor and get dataset (the synchronous shortcut) or Run Actor (async, pairs with a separate “Get Dataset Items” node downstream)
• Actor: DevilScrapes/google-ads-transparency (or any Devil Scrapes slug)
• Input: the same JSON body you’d send over the API, e.g. {"searchDomains": ["nike.com"], "maxResults": 50}

A typical pattern: Schedule Trigger → Apify node (run Actor) → Function node (reshape rows) → Google Sheets / Slack / database node. Because the async Run Actor operation can take a while on a large scrape, put a Wait node or a separate polling loop in front of the dataset-fetch step if you’re not using the synchronous variant — the same async-vs-sync tradeoff covered in Synchronous runs, webhooks, and scheduling applies inside n8n too.

If your n8n instance doesn’t have the Apify node available, an HTTP Request node pointed at https://api.apify.com/v2/acts/{actorId}/run-sync-get-dataset-items with your token in the Authorization header works identically — n8n’s Apify node is a convenience wrapper around the same REST endpoints from Run any DevilScrapes Actor via the API.

Make (formerly Integromat)

Make has an Apify app in its module library. The two modules you’ll use most:

• Run an Actor — starts a run; configure the Actor ID and a JSON input map.
• Get Dataset Items — pulls the results once the run is done, feeding them into the rest of your Make scenario as a bundle you can iterate over.

Chain them with Make’s built-in Sleep/Repeater modules if you’re polling manually, or point the scenario’s webhook trigger at Apify’s webhook URL (see Synchronous runs, webhooks, and scheduling) to react the instant a run finishes instead of polling on a timer.

Zapier

Apify’s Zapier integration exposes Run Actor as an action step. Because Zaps generally expect a fast response, prefer:

• A synchronous Actor call for small, quick inputs, or
• A two-step Zap where step one starts the run and a webhook trigger Zap (a separate Zap listening for the Apify webhook) picks up when it’s done, rather than one long-held Zap step waiting on a big scrape.

Map the dataset’s JSON fields to whatever downstream app you’re feeding — a CRM, a spreadsheet, an email tool — the same way you’d map any other Zapier action’s output fields.

Google Sheets

Two ways to land results in a spreadsheet:

1. Automation-tool route: any of the above (n8n, Make, Zapier) can write dataset rows directly into a Google Sheet as a downstream step.
2. Direct import: every dataset has a stable export URL. In Google Sheets, use IMPORTDATA() pointed at the CSV export:

=IMPORTDATA("https://api.apify.com/v2/datasets/<datasetId>/items?format=csv&clean=1")

This works for a one-off pull of a dataset you already have the ID for. For a recurring feed, prefer the automation-tool route above so a scheduled run automatically refreshes the sheet rather than requiring you to re-trigger IMPORTDATA manually.

Direct dataset URL: CSV, JSON, XLSX

Every dataset is reachable at GET /v2/datasets/{datasetId}/items with a format query parameter: json, jsonl, csv, or xlsx. This is the lowest-friction integration path if your destination just needs a file, not an API call:

curl "https://api.apify.com/v2/datasets/<datasetId>/items?format=xlsx&clean=1" -o results.xlsx

Point this URL at anything that can fetch a file over HTTP — a scheduled task in a BI tool, a data-warehouse loader, a plain download link you hand to a teammate. clean=1 strips Apify’s internal metadata fields so you get just the result rows. See Export formats and data quality for the full format list and field conventions.

Embedding in your own pipeline

If you’re building a proper data pipeline (Airflow, Dagster, a cron-driven script, a queue worker), the pattern is the same one covered in Run any DevilScrapes Actor via the API: call the run endpoint from your job, wait or poll, pull the dataset, load it into your warehouse or downstream service. Apify’s official Python and JavaScript clients slot into most pipeline frameworks without any special-casing — they’re just HTTP calls with retry logic built in.

FAQ

Do I need to write code to use n8n, Make, or Zapier with a Devil Scrapes Actor?

No — each platform has a point-and-click Apify integration (native app or HTTP Request node) that just needs your API token and the Actor’s slug. No client library required.

Which is better for a scheduled daily scrape, an automation tool or Apify Schedules?

Either works. Apify Schedules (see Synchronous runs, webhooks, and scheduling) is simplest if you just need the run to fire on a cadence with a fixed input. Use an automation tool instead if you need conditional logic, multiple downstream steps, or varying inputs per run.

Can I get results straight into a Google Sheet without an automation tool?

Yes, with IMPORTDATA() pointed at the dataset’s CSV export URL for a one-off pull. For a sheet that refreshes automatically on a schedule, route through n8n, Make, or Zapier instead.

What format should I use for a BI tool import?

CSV or XLSX cover most BI tools directly. If your tool can parse JSON natively, JSON or JSONL preserves nested fields (like a list of tags) more faithfully than the flattened CSV export.

Is there a size limit on what I can pull through these integrations?

The dataset endpoints paginate large results automatically via limit/offset, and most automation-tool connectors handle that pagination for you. For very large exports, the direct-download route (CSV/XLSX file) is usually simpler than an automation tool trying to hold the whole result set in memory.